[what is machine learning？]

1.2 [what is machine learning？]

1.人：observation --> learing --> skill

机器：data --> ML --> improved performance measure /skill

2.什么情况下适合使用机器学习：

（1）some 'underlying pattern' to be learned

（2）not easy（programmable） definition ：不是很容易写出一些规则去处理

（3）data about the pattern : inputs

3.example（best suited ML）：

（1）预测婴儿在下一次哪个时间点会哭？ no: (1)no pattern

（2）判断一个图像中是否包含圆形？ yes no: (2)很容易写definitioin/program

（3）判断是否给一个用户发放信用卡？ yes :(1)user behavior (2)not easily program（3）data

（4）地球是否hi在未来十年因为滥用核能而毁灭？ no: (3)no data yet

1.3[applications of ML]

1.Food(某家餐厅是否会引起食物中毒)

data:twitter+location

skill:tell food poisoning likeliness of restaurant

2.Clothing

data:sales figures销售数据 + client surveys顾客喜好

skill:give good recommendations to clients

3.Housing

data:characteristics of building and their energy load耗能状况

skill:predict energy load of other buildings closely

4.transportation

data:traffic sign images and meanings交通标志

skill:recognize traffic signs accurately

5.Education

data:students' records on quizzes on a math tutoring system

skill：predict whether a student can give a correct answer to another quiz question

answer correctly~~[recent strength of student > difficulty of question]

data:9 million records from students

ML determines(reverse-engineers) strength and difficult auto

6.Entertainment

data:how many users have rated some movies

skill:predict how a user would rate an unrated movie

data: 1亿 ratings that 480,189 users gave to 17,770 movies(Netflix 线上租赁DVD)

1.4Formalize the learning problem

input:x->X

output:y->Y

f:X->Y

data: D{(x1,y1),(x2,y2),,,}

hypothesis -> skill g:x->y

{(x n , y n )} from f -->ML--> g

A:algorithm

H:hypothesis 利用A从H的众多假设里选择一个最接近f的g.

1.5data mining数据挖掘/AI:Artificial Intelligence/Statistics

DM :use huge data to find property that is interesting

ML = DM(KDDCups)

AI：

ML can realize AI,

eg. 下棋：（传统方法：game tree; ML： learning from board data)

Statistics：use data to make inference about unknown process

g is an inference outcome(预测推论的结果) ；f is something unknown

statistics can be used to achieve ML

转载于:https://www.cnblogs.com/mengxiangjialzh/p/8325363.html

[what is machine learning？]相关推荐

week one（1）—What is machine learning？
1 介绍了机器学习 What is machine learning? 2 .Supervised Learning 监督学习 (1)聚类算法 (2) 鸡尾酒晚会问题转载于:https://ww ...
Machine Learning:如何选择机器学习算法？
2019独角兽企业重金招聘Python工程师标准>>> Machine Learning Algorithms Overview 关于目前最流行的一些机器学习算法,建议阅读: Mac ...
AI：《Why is DevOps for Machine Learning so Different?—为什么机器学习的 DevOps 如此不同？》翻译与解读
AI:<Why is DevOps for Machine Learning so Different?-为什么机器学习的 DevOps 如此不同?>翻译与解读目录 <Why is ...
数据挖掘(data mining)，机器学习(machine learning)，和人工智能(AI)的区别是什么？数据科学(data science)和商业分析(business analytics
数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)的区别是什么? 数据科学(data science)和商业分析(business analytics ...
如何下载 International Conference on Machine Learning（ICML）顶会的论文？从哪找ICML顶会论文？如何判断会议和期刊级别？
之前费了老长时间都找不到ICML会议的论文,我有找过ACM数据库,但是ACM数据库只有2009年以前的论文集,就很离谱,今天终于被我找到了下载ICML会议的方法,不光ICML会议的论文可以下载,其他顶 ...
bff v2ex_语音备忘录的BFF-如何通过Machine Learning简化Speech2Text
bff v2ex by Rafael Belchior 通过拉斐尔·贝尔基奥尔(Rafael Belchior) 语音备忘录的BFF-如何通过Machine Learning简化Speech2Text ...
Auto Machine Learning 自动化机器学习笔记
⭐适读人群:有机器学习算法基础 1. auto-sklearn 能 auto 到什么地步? 在机器学习中的分类模型中: 常规 ML framework 如下图灰色部分:导入数据-数据清洗-特征工程-分 ...
机器学习与优化基础（Machine Learning and Optimization）
点击上方"小白学视觉",选择加"星标"或"置顶" 重磅干货,第一时间送达本文转自|新机器视觉引用大佬Pedro Domingos的说法: ...
Machine Learning | (9) 回归算法-线性回归
Machine Learning | 机器学习简介 Machine Learning | (1) Scikit-learn与特征工程 Machine Learning | (2) sklearn数据集 ...
Machine Learning | (5) Scikit-learn的分类器算法-朴素贝叶斯
Machine Learning | 机器学习简介 Machine Learning | (1) Scikit-learn与特征工程 Machine Learning | (2) sklearn数据集 ...

[what is machine learning？]

[what is machine learning？]相关推荐

最新文章

热门文章